Electronic system for automatically generating resource distributions based on SMS-based instructions using machine learning

ABSTRACT

Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and determine, based on the text-based instruction, a sender alias from which the text-based instruction was sent. The present invention may be configured to determine, based on user data in a user information data structure, a user associated with the sender alias, determine, based on user data associated with the user in the user information data structure and based on predicted distribution elements from a machine learning model, actual distribution elements, and generate, based on the actual distribution elements, a resource distribution.

FIELD OF THE INVENTION

The present invention embraces an electronic system for automaticallygenerating resource distributions based on SMS-based instructions usingmachine learning.

BACKGROUND

A sending user may provide, to an entity, text-based instructionsrequesting that the entity distribute resources from a source retainermanaged by the entity and associated with the sending user to anothersource retainer associated with a recipient user. Another userassociated with the entity may read the text-based instructions, searchone or more databases to identify data associated with the sending user,the source retainer, the recipient user, and the other source retainer,and input, to a system associated with the entity, the data associatedwith the sending user, the source retainer, the recipient user, and theother source retainer to generate a resource distribution.

SUMMARY

The following presents a simplified summary of one or more embodimentsof the present invention, in order to provide a basic understanding ofsuch embodiments. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor delineate the scope of any orall embodiments. This summary presents some concepts of one or moreembodiments of the present invention in a simplified form as a preludeto the more detailed description that is presented later.

In one aspect, the present invention embraces a system for automaticallygenerating resource distributions. The system may include at least onenon-transitory storage device and at least one processing device coupledto the at least one non-transitory storage device, where the at leastone processing device is configured to receive a text-based instruction,determine, based on the text-based instruction, a sender alias fromwhich the text-based instruction was sent, determine, based on user datain a user information data structure, a user associated with the senderalias, determine, based on user data associated with the user in theuser information data structure and based on predicted distributionelements from a machine learning model, actual distribution elements,and generate, based on the actual distribution elements, a resourcedistribution.

In some embodiments, the text-based instruction includes at least one ofan email message, an SMS message, recorded speech converted to text,text input to a chat function, or text recognized in an image.

In some embodiments, the predicted distribution elements include atleast one of a predicted sending source retainer, a predicted receivingsource retainer, a predicted amount of resources to be distributed, apredicted type of resource distribution, a predicted type of resourcesto be distributed, a predicted date on which resources are to bedistributed, a predicted frequency at which resources are to bedistributed, a predicted type of sending source retainer, or a predictedtype of receiving source retainer.

In some embodiments, the at least one processing device is furtherconfigured to parse, using the machine learning model, the text-basedinstruction to determine additional information associated with thetext-based instruction. Additionally, or alternatively, the additionalinformation includes at least one of the sender alias, a recipient aliasto which the text-based instruction was sent, an entity associated withthe resource distribution, a name of the user, an address of the user,or content of the text-based instruction. In some embodiments, the atleast one processing device is configured to, when determining the userassociated with the sender alias, determine, based on the user data inthe user information data structure and based on the additionalinformation, the user associated with the sender alias. Additionally, oralternatively, the at least one processing device is configured to, whendetermining the actual distribution elements, determine, based on theuser data in the user information data structure associated with theuser, based on the predicted distribution elements, and based on theadditional information, the actual distribution elements.

In some embodiments, the actual distribution elements include at leastone of an actual sending source retainer, an actual receiving sourceretainer, an actual amount of resources to be distributed, an actualtype of resource distribution, an actual type of resources to bedistributed, an actual date on which resources are to be distributed, anactual frequency at which resources are to be distributed, an actualtype of sending source retainer, or an actual type of receiving sourceretainer.

In some embodiments, the user information data structure includes datathat, for a plurality of users, associates users with aliases,associates users to source retainer officers, associates users to sourceretainer roles, associates users to other users, associates sourceretainers to distribution eligibility rules, or associates users andsource retainers to historical resource distributions. Additionally, oralternatively, the at least one processing device is configured to, whendetermining the actual distribution elements, determine source retainersassociated with the user, determine source retainers for which the useris authorized to conduct resource distributions, determine sourceretainers of other users associated with the user, determine sourceretainers eligible for resource distributions via text-basedinstructions, and determine source retainers associated with historicalresource distributions conducted by the user.

In some embodiments, the at least one processing device is configuredto, when determining the actual distribution elements, determine that apredicted distribution element, of the predicted distribution elements,is a partial distribution element and determine, based on the user datain the user information data structure associated with the user andbased on the partial distribution element, an actual distributionelement of the actual distribution elements.

In some embodiments, the at least one processing device is configuredto, when determining the actual distribution elements, determine thatmultiple possible distribution elements are associated with a predicteddistribution element of the predicted distribution elements, determine,based on a frequency of usage of the multiple possible distributionelements and based on a timing of usage of the multiple possibledistribution elements, a likelihood of each of the multiple possibledistribution elements corresponding to the predicted distributionelement, and provide, to another user and in an order based on thelikelihood of each of the multiple possible distribution elementscorresponding to the predicted distribution element, the multiplepossible distribution elements for selection by the other user forgeneration in the resource distribution.

In some embodiments, the at least one processing device is configured toprovide, to another user, the resource distribution for authorization.

In some embodiments, the at least one processing device is configured toperform the resource distribution.

In another aspect, the present invention embraces a computer programproduct for automatically generating resource distributions. Thecomputer program product may include a non-transitory computer-readablemedium including code causing a first apparatus to receive a text-basedinstruction, determine, based on the text-based instruction, a senderalias from which the text-based instruction was sent, determine, basedon user data in a user information data structure, a user associatedwith the sender alias, determine, based on user data associated with theuser in the user information data structure and based on predicteddistribution elements from a machine learning model, actual distributionelements, and generate, based on the actual distribution elements, aresource distribution.

In some embodiments, the text-based instruction includes at least one ofan email message, an SMS message, recorded speech converted to text,text input to a chat function, or text recognized in an image.

In some embodiments, the predicted distribution elements include atleast one of a predicted sending source retainer, a predicted receivingsource retainer, a predicted amount of resources to be distributed, apredicted type of resource distribution, a predicted type of resourcesto be distributed, a predicted date on which resources are to bedistributed, a predicted frequency at which resources are to bedistributed, a predicted type of sending source retainer, or a predictedtype of receiving source retainer.

In some embodiments, the non-transitory computer-readable mediumincludes code causing the first apparatus to parse, using the machinelearning model, the text-based instruction to determine additionalinformation associated with the text-based instruction.

In some embodiments, the additional information includes at least one ofthe sender alias, a recipient alias to which the text-based instructionwas sent, an entity associated with the resource distribution, a name ofthe user, an address of the user, or content of the text-basedinstruction.

In yet another aspect, the present invention embraces a method forautomatically generating resource distributions. The method may includereceiving a text-based instruction, parsing, using a machine learningmodel, the text-based instruction to generate a structured resourcedistribution including predicted distribution elements, determining,based on the text-based instruction, a sender alias from which thetext-based instruction was sent, determining, based on user data in auser information data structure, a user associated with the senderalias, determining, based on user data associated with the user in theuser information data structure and based on the predicted distributionelements, actual distribution elements, and generating, based on theactual distribution elements, a resource distribution.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined with yet other embodiments, further details of whichmay be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made the accompanying drawings, wherein:

FIG. 1 illustrates technical components of a system for automaticallygenerating resource distributions, in accordance with an embodiment ofthe invention;

FIG. 2 illustrates a process flow for automatically generating resourcedistributions, in accordance with an embodiment of the invention; and

FIG. 3 illustrates a process flow for automatically generating resourcedistributions, in accordance with an embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Where possible, any terms expressed in the singularform herein are meant to also include the plural form and vice versa,unless explicitly stated otherwise. Also, as used herein, the term “a”and/or “an” shall mean “one or more,” even though the phrase “one ormore” is also used herein. Furthermore, when it is said herein thatsomething is “based on” something else, it may be based on one or moreother things as well. In other words, unless expressly indicatedotherwise, as used herein “based on” means “based at least in part on”or “based at least partially on.” Like numbers refer to like elementsthroughout.

As used herein, an “entity” may be any institution employing informationtechnology resources and particularly technology infrastructureconfigured for processing large amounts of data. Typically, the data maybe related to products, services, and/or the like offered and/orprovided by the entity, customers of the entity, other aspect of theoperations of the entity, people who work for the entity, and/or thelike. As such, the entity may be an institution, group, association,financial institution, establishment, company, union, authority,merchant, service provider, and/or or the like, employing informationtechnology resources for processing large amounts of data.

As used herein, a “user” may be an individual associated with an entity.As such, in some embodiments, the user may be an individual having pastrelationships, current relationships or potential future relationshipswith an entity. In some embodiments, a “user” may be an employee (e.g.,an associate, a project manager, a manager, an administrator, aninternal operations analyst, and/or the like) of the entity and/orenterprises affiliated with the entity, capable of operating systemsdescribed herein. In some embodiments, a “user” may be any individual,another entity, and/or a system who has a relationship with the entity,such as a customer, a prospective customer, and/or the like. In someembodiments, a user may be a system performing one or more tasksdescribed herein.

As used herein, a “user interface” may be any device or software thatallows a user to input information, such as commands and/or data, into adevice, and/or that allows the device to output information to the user.For example, a user interface may include a graphical user interface(GUI) and/or an interface to input computer-executable instructions thatdirect a processing device to carry out functions. The user interfacemay employ input and/or output devices to input data received from auser and/or output data to a user. Input devices and/or output devicesmay include a display, mouse, keyboard, button, touchpad, touch screen,microphone, speaker, LED, light, joystick, switch, buzzer, bell, and/orother devices for communicating with one or more users.

As used herein, a “resource” may generally refer to objects, products,devices, goods, commodities, services, offers, discounts, currency,cash, cash equivalents, rewards, reward points, benefit rewards, bonusmiles, cash back, credits, and/or the like, and/or the ability andopportunity to access and use the same. Some example implementationsherein contemplate property held by a user, including property that isstored and/or maintained by a third-party entity. In some exampleimplementations, a resource may be associated with one or more accountsor may be property that is not associated with a specific account.Examples of resources associated with accounts may be accounts that havecash or cash equivalents, commodities, and/or accounts that are fundedwith or contain property, such as safety deposit boxes containingjewelry, art or other valuables, a trust account that is funded withproperty, and/or the like.

As used herein, a “source retainer” may generally refer to an account, asystem, and/or the like associated with a user and/or a type ofresources, such as a checking account, a deposit account, a savingsaccount, a credit account, a rewards account, a rewards points account,a benefit rewards account, a bonus miles account, a cash back account,and/or the like, which may be managed and/or maintained by an entity,such as a financial institution, an electronic resource transferinstitution (e.g., a credit card company, a debit card company, aprepaid card company, and/or the like), a credit union, and/or the like.

As used herein, a “distribution” and/or an “allocation” may refer to anytransaction, activities, and/or communication between one or moreentities, between a user and one or more entities, and/or the like. Aresource distribution and/or an allocation of resources may refer to anydistribution of resources such as, but not limited to, a payment,processing of funds, purchase of goods or services, a return of goods orservices, a payment transaction, a credit transaction, otherinteractions involving a user's resource or account, and/or the like. Inthe context of an entity such as a financial institution, a resourcedistribution and/or an allocation of resources may refer to one or moreof a sale of goods and/or services, initiating an automated tellermachine (ATM) or online financial session, an account balance inquiry, arewards transfer, an account money transfer or withdrawal, opening afinancial application on a user's computer or mobile device, a useraccessing their e-wallet, any other interaction involving the userand/or the user's device that invokes and/or is detectable by thefinancial institution, and/or the like. In some embodiments, the usermay authorize a resource distribution and/or an allocation of resourcesusing a resource distribution instrument (e.g., credit cards, debitcards, checks, digital wallets, currency, loyalty points, and/or thelike) and/or resource distribution credentials (e.g., account numbers,resource distribution instrument identifiers, and/or the like). Aresource distribution and/or an allocation of resources may include oneor more of the following: renting, selling, and/or leasing goods and/orservices (e.g., groceries, stamps, tickets, DVDs, vending machine items,and/or the like); making payments to creditors (e.g., paying monthlybills; paying federal, state, and/or local taxes, and/or the like);sending remittances; loading money onto stored value cards (SVCs) and/orprepaid cards; donating to charities; and/or the like. Unlessspecifically limited by the context, a “resource distribution,” an“allocation of resources,” a “resource transfer,” a “transaction,” a“transaction event,” and/or a “point of transaction event” may refer toany activity between a user, a merchant, an entity, and/or the like. Insome embodiments, a resource distribution and/or an allocation ofresources may refer to financial transactions involving direct orindirect movement of funds through traditional paper transactionprocessing systems (e.g., paper check processing) or through electronictransaction processing systems. In this regard, resource distributionsand/or allocations of resources may refer to the user initiating apurchase for a product, service, and/or the like from a merchant.Financial resource distribution and/or financial allocations ofresources may include point of sale (POS) transactions, automated tellermachine (ATM) transactions, person-to-person (P2P) transfers, internettransactions, online shopping, electronic funds transfers betweenaccounts, transactions with a financial institution teller, personalchecks, conducting purchases using loyalty/rewards points, and/or thelike. When describing that resource transfers or transactions areevaluated, such descriptions may mean that the transaction has alreadyoccurred, is in the process of occurring and/or being processed, and/orhas yet to be processed/posted by one or more financial institutions.

As used herein, “resource distribution instrument” may refer to anelectronic payment vehicle, such as an electronic credit card, debitcard, and/or the like, associated with a source retainer (e.g., achecking account, a deposit account, a savings account, a creditaccount, and/or the like). In some embodiments, the resourcedistribution instrument may not be a “card” and may instead be accountidentifying information stored electronically in a user device, such aspayment credentials and/or tokens and/or aliases associated with adigital wallet, account identifiers stored by a mobile application,and/or the like.

In some embodiments, the term “module” with respect to an apparatus mayrefer to a hardware component of the apparatus, a software component ofthe apparatus, and/or a component of the apparatus that includes bothhardware and software. In some embodiments, the term “chip” may refer toan integrated circuit, a microprocessor, a system-on-a-chip, amicrocontroller, and/or the like that may either be integrated into theexternal apparatus, may be inserted and/or removed from the externalapparatus by a user, and/or the like.

As used herein, an “engine” may refer to core elements of a computerprogram, part of a computer program that serves as a foundation for alarger piece of software and drives the functionality of the software,and/or the like. An engine may be self-contained but may includeexternally-controllable code that encapsulates powerful logic designedto perform or execute a specific type of function. In one aspect, anengine may be underlying source code that establishes file hierarchy,input and/or output methods, how a part of a computer program interactsand/or communicates with other software and/or hardware, and/or thelike. The components of an engine may vary based on the needs of thecomputer program as part of the larger piece of software. In someembodiments, an engine may be configured to retrieve resources createdin other computer programs, which may then be ported into the engine foruse during specific operational aspects of the engine. An engine may beconfigurable to be implemented within any general-purpose computingsystem. In doing so, the engine may be configured to execute source codeembedded therein to control specific features of the general-purposecomputing system to execute specific computing operations, therebytransforming the general-purpose system into a specific purposecomputing system.

As used herein, a “component” of an application may include a softwarepackage, a service, a resource, a module, and/or the like that includesa set of related functions and/or data. In some embodiments, a componentmay provide a source capability (e.g., a function, a business function,and/or the like) to an application including the component. In someembodiments, components of an application may communicate with eachother via interfaces and may provide information to each otherindicative of the services and/or functions that other components mayutilize and/or how other components may utilize the services and/orfunctions. Additionally, or alternatively, components of an applicationmay be substitutable such that a component may replace anothercomponent. In some embodiments, components may include objects,collections of objects, and/or the like.

As used herein, “authentication credentials” may be any information thatmay be used to identify a user. For example, a system may prompt a userto enter authentication information such as a username, a password, apersonal identification number (PIN), a passcode, biometric information(e.g., voice authentication, a fingerprint, and/or a retina scan), ananswer to a security question, a unique intrinsic user activity, such asmaking a predefined motion with a user device, and/or the like. Theauthentication information may be used to authenticate the identity ofthe user (e.g., determine that the authentication information isassociated with an account) and/or determine that the user has authorityto access an account or system. In some embodiments, the system may beowned and/or operated by an entity. In such embodiments, the entity mayemploy additional computer systems, such as authentication servers, tovalidate and certify resources inputted by a plurality of users withinthe system. The system may further use authentication servers to certifythe identity of users of the system, such that other users may verifythe identity of the certified users. In some embodiments, the entity maycertify the identity of the users. Furthermore, authenticationinformation and/or permission may be assigned to and/or required from auser, application, computing node, computing cluster, and/or the like toaccess stored data within at least a portion of the system.

As used herein, an “interaction” may refer to any communication betweenone or more users, one or more entities or institutions, and/or one ormore devices, nodes, clusters, and/or systems within the systemenvironment described herein. For example, an interaction may refer to atransfer of data between devices, an accessing of stored data by one ormore nodes of a computing cluster, a transmission of a requested task,and/or the like. In some embodiments, an interaction may refer to anentity, a user, a system, and/or a device providing an advertisement,information, data, a user interface, and/or the like to another entity,another user, another system, and/or another device.

As noted, a sending user (e.g., a client, a payor, and/or the like) mayprovide, to an entity (e.g., a financial institution and/or the like),text-based instructions requesting that the entity distribute resources(e.g., conduct a wire transfer, conduct a transaction, transfer funds,and/or the like) from a source retainer (e.g., a checking account, adeposit account, a savings account, a credit account, and/or the like)managed by the entity and associated with the sending user to anothersource retainer associated with a recipient user (e.g., a payee and/orthe like). Another user associated with the entity (e.g., an associate,an employee, and/or the like) may read the text-based instructions,search one or more databases to identify data associated with thesending user, the source retainer, the recipient user, and the othersource retainer, and input, to a system associated with the entity, thedata associated with the sending user, the source retainer, therecipient user, and the other source retainer to generate a resourcedistribution. However, searching databases to identify various datainputs to complete the resource distribution consumes computingresources (e.g., processing resources, memory resources, powerresources, communication resources, and/or the like) and/or networkresources. Furthermore, receiving a high volume of text-basedinstructions requires a plurality of users associated with the entitythereby consuming financial resources, requires that the plurality ofusers associated with the entity read all of the text-based instructionsthereby consuming additional financial resources, and requires that theplurality of users associated with the entity manually input variousdata inputs to complete the resource distributions thereby increasing alikelihood of errors.

Some embodiments described herein provide a system, a computer programproduct, and/or a method automatically generating resourcedistributions. For example, a system (e.g., an electronic system forautomatically generating resource distributions based on SMS-basedinstructions using machine learning and/or the like) may be configuredto receive text-based instructions (e.g., email messages, SMS messages,recorded speech converted to text, text input to a chat function, textrecognized in images, and/or the like) and parse, using a machinelearning model, the text-based instructions to generate a structuredresource distribution including predicted distribution elements. Thesystem may be configured to determine, based on user data associatedwith a user in the user information data structure and based on thepredicted distribution elements, actual distribution elements andgenerate, based on the actual distribution elements, a resourcedistribution. In some embodiments, the system may be configured to parsethe text-based instructions to obtain partial information regarding oneor more elements of the resource distribution (e.g., part of an accountnumber, a first name but not a last name, and/or the like) anddetermine, based on user data in the user information data structure andthe partial information, complete information (e.g., a full accountnumber, a first and last name, and/or the like) that may be used as anactual distribution element in a resource distribution. Additionally, oralternatively, the system may be configured to determine that multiplepossible distribution elements are associated with a predicteddistribution element obtained by parsing the instructions, determine,based on a frequency of usage of the multiple possible distributionelements and based on a timing of usage of the multiple possibledistribution elements, a likelihood of each of the multiple possibledistribution elements corresponding to the predicted distributionelement, and provide, to a user associated with an entity (e.g., anassociate, an employee, and/or the like) and in an order based on thelikelihood of each of the multiple possible distribution elementscorresponding to the predicted distribution element, the multiplepossible distribution elements for selection by the user for generationin the resource distribution (e.g., via a drop-down menu and/or thelike).

By parsing the text-based instruction, determining actual distributionelements from user data, and/or generating the resource distribution,the system conserves the computing resources (e.g., processingresources, memory resources, power resources, communication resources,and/or the like) and/or network resources that would otherwise beconsumed by searching databases to identify various data inputs tocomplete the resource distribution. Furthermore, the system may receivea high volume of text-based instructions and accurately generateresource distributions thereby conserving the financial resources thatwould otherwise be consumed by requiring a plurality of users to readthe text-based instructions and provide inputs to generate the resourcedistributions.

FIG. 1 presents an exemplary block diagram of a system environment 100for automatically generating resource distributions within a technicalenvironment, in accordance with an embodiment of the invention. FIG. 1provides a system environment 100 that includes specialized servers anda system communicably linked across a distributive network of nodesrequired to perform functions of process flows described herein inaccordance with embodiments of the present invention.

As illustrated, the system environment 100 includes a network 110, asystem 130, and a user input system 140. Also shown in FIG. 1 is a userof the user input system 140. The user input system 140 may be a mobiledevice, a non-mobile computing device, and/or the like. The user may bea person who uses the user input system 140 to access, view modify,interact with, and/or the like information, data, images, video, and/orthe like. The one or more applications may be configured to communicatewith the system 130, input information onto a user interface presentedon the user input system 140, and/or the like. The applications storedon the user input system 140 and the system 130 may incorporate one ormore parts of any process flow described herein.

As shown in FIG. 1 , the system 130 and the user input system 140 areeach operatively and selectively connected to the network 110, which mayinclude one or more separate networks. In some embodiments, the network110 may include a telecommunication network, local area network (LAN), awide area network (WAN), and/or a global area network (GAN), such as theInternet. Additionally, or alternatively, the network 110 may be secureand/or unsecure and may also include wireless and/or wired and/oroptical interconnection technology.

In some embodiments, the system 130 and the user input system 140 may beused to implement processes described herein, including user-side andserver-side processes for automatically generating resourcedistributions, in accordance with an embodiment of the presentinvention. The system 130 may represent various forms of digitalcomputers, such as laptops, desktops, workstations, personal digitalassistants, servers, blade servers, mainframes, and/or the like. Theuser input system 140 may represent various forms of mobile devices,such as personal digital assistants, cellular telephones, smartphones,smart glasses, and/or the like. The components shown here, theirconnections, their relationships, and/or their functions, are meant tobe exemplary only, and are not meant to limit implementations of theinventions described and/or claimed in this document.

In some embodiments, the system 130 may include a processor 102, memory104, a storage device 106, a high-speed interface 108 connecting tomemory 104, high-speed expansion ports 111, and a low-speed interface112 connecting to low-speed bus 114 and storage device 106. Each of thecomponents 102, 104, 106, 108, 111, and 112 may be interconnected usingvarious buses, and may be mounted on a common motherboard or in othermanners as appropriate. The processor 102 may process instructions forexecution within the system 130, including instructions stored in thememory 104 and/or on the storage device 106 to display graphicalinformation for a GUI on an external input/output device, such as adisplay 116 coupled to a high-speed interface 108. In some embodiments,multiple processors, multiple buses, multiple memories, multiple typesof memory, and/or the like may be used. Also, multiple systems, same orsimilar to system 130 may be connected, with each system providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, a multi-processor system, and/or the like). In someembodiments, the system 130 may be managed by an entity, such as abusiness, a merchant, a financial institution, a card managementinstitution, an software and/or hardware development company, a softwareand/or hardware testing company, and/or the like. The system 130 may belocated at a facility associated with the entity and/or remotely fromthe facility associated with the entity.

The memory 104 may store information within the system 130. In oneimplementation, the memory 104 may be a volatile memory unit or units,such as volatile random-access memory (RAM) having a cache area for thetemporary storage of information. In another implementation, the memory104 may be a non-volatile memory unit or units. The memory 104 may alsobe another form of computer-readable medium, such as a magnetic oroptical disk, which may be embedded and/or may be removable. Thenon-volatile memory may additionally or alternatively include an EEPROM,flash memory, and/or the like. The memory 104 may store any one or moreof pieces of information and data used by the system in which it residesto implement the functions of that system. In this regard, the systemmay dynamically utilize the volatile memory over the non-volatile memoryby storing multiple pieces of information in the volatile memory,thereby reducing the load on the system and increasing the processingspeed.

The storage device 106 may be capable of providing mass storage for thesystem 130. In one aspect, the storage device 106 may be or contain acomputer-readable medium, such as a floppy disk device, a hard diskdevice, an optical disk device, a tape device, a flash memory and/orother similar solid state memory device, and/or an array of devices,including devices in a storage area network or other configurations. Acomputer program product may be tangibly embodied in an informationcarrier. The computer program product may also contain instructionsthat, when executed, perform one or more methods, such as thosedescribed herein. The information carrier may be a non-transitorycomputer-readable or machine-readable storage medium, such as the memory104, the storage device 106, and/or memory on processor 102.

In some embodiments, the system 130 may be configured to access, via thenetwork 110, a number of other computing devices (not shown). In thisregard, the system 130 may be configured to access one or more storagedevices and/or one or more memory devices associated with each of theother computing devices. In this way, the system 130 may implementdynamic allocation and de-allocation of local memory resources amongmultiple computing devices in a parallel and/or distributed system.Given a group of computing devices and a collection of interconnectedlocal memory devices, the fragmentation of memory resources is renderedirrelevant by configuring the system 130 to dynamically allocate memorybased on availability of memory either locally, or in any of the othercomputing devices accessible via the network. In effect, the memory mayappear to be allocated from a central pool of memory, even though thememory space may be distributed throughout the system. Such a method ofdynamically allocating memory provides increased flexibility when thedata size changes during the lifetime of an application and allowsmemory reuse for better utilization of the memory resources when thedata sizes are large.

The high-speed interface 108 may manage bandwidth-intensive operationsfor the system 130, while the low-speed interface 112 and/or controllermanages lower bandwidth-intensive operations. Such allocation offunctions is exemplary only. In some embodiments, the high-speedinterface 108 is coupled to memory 104, display 116 (e.g., through agraphics processor or accelerator), and to high-speed expansion ports111, which may accept various expansion cards (not shown). In someembodiments, low-speed interface 112 and/or controller is coupled tostorage device 106 and low-speed bus 114 (e.g., expansion port). Thelow-speed bus 114, which may include various communication ports (e.g.,USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one ormore input/output devices, such as a keyboard, a pointing device, ascanner, and/or a networking device such as a switch or router (e.g.,through a network adapter).

The system 130 may be implemented in a number of different forms, asshown in FIG. 1 . For example, it may be implemented as a standardserver or multiple times in a group of such servers. Additionally, oralternatively, the system 130 may be implemented as part of a rackserver system, a personal computer, such as a laptop computer, and/orthe like. Alternatively, components from system 130 may be combined withone or more other same or similar systems and the user input system 140may be made up of multiple computing devices communicating with eachother.

FIG. 1 also illustrates a user input system 140, in accordance with anembodiment of the invention. The user input system 140 may include aprocessor 152, memory 154, an input/output device such as a display 156,a communication interface 158, and a transceiver 160, among othercomponents, such as one or more image sensors. The user input system 140may also be provided with a storage device, such as a microdrive and/orthe like, to provide additional storage. Each of the components 152,154, 158, and 160, may be interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 152 may be configured to execute instructions within theuser input system 140, including instructions stored in the memory 154.The processor 152 may be implemented as a chipset of chips that includeseparate and multiple analog and/or digital processors. The processor152 may be configured to provide, for example, for coordination of theother components of the user input system 140, such as control of userinterfaces, applications run by user input system 140, and/or wirelesscommunication by user input system 140.

The processor 152 may be configured to communicate with the user throughcontrol interface 164 and display interface 166 coupled to a display156. The display 156 may be, for example, a Thin-Film-Transistor LiquidCrystal Display (TFT LCD) or an Organic Light Emitting Diode (OLED)display, and/or other appropriate display technology. An interface ofthe display 156 may include appropriate circuitry and may be configuredfor driving the display 156 to present graphical and other informationto a user. The control interface 164 may receive commands from a userand convert them for submission to the processor 152. In addition, anexternal interface 168 may be provided in communication with processor152 to enable near area communication of user input system 140 withother devices. External interface 168 may provide, for example, forwired communication in some implementations, or for wirelesscommunication in other implementations, and multiple interfaces may alsobe used.

The memory 154 may store information within the user input system 140.The memory 154 may be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory may also be provided andconnected to user input system 140 through an expansion interface (notshown), which may include, for example, a Single In Line Memory Module(SIMM) card interface. Such expansion memory may provide extra storagespace for user input system 140 and/or may store applications and/orother information therein. In some embodiments, expansion memory mayinclude instructions to carry out or supplement the processes describedabove and/or may include secure information. For example, expansionmemory may be provided as a security module for user input system 140and may be programmed with instructions that permit secure use of userinput system 140. Additionally, or alternatively, secure applicationsmay be provided via the SIMM cards, along with additional information,such as placing identifying information on the SIMM card in a securemanner. In some embodiments, the user may use applications to executeprocesses described with respect to the process flows described herein.For example, one or more applications may execute the process flowsdescribed herein. In some embodiments, one or more applications storedin the system 130 and/or the user input system 140 may interact with oneanother and may be configured to implement any one or more portions ofthe various user interfaces and/or process flow described herein.

The memory 154 may include, for example, flash memory and/or NVRAMmemory. In some embodiments, a computer program product may be tangiblyembodied in an information carrier. The computer program product maycontain instructions that, when executed, perform one or more methods,such as those described herein. The information carrier may be acomputer-readable or machine-readable medium, such as the memory 154,expansion memory, memory on processor 152, and/or a propagated signalthat may be received, for example, over transceiver 160 and/or externalinterface 168.

In some embodiments, the user may use the user input system 140 totransmit and/or receive information and/or commands to and/or from thesystem 130. In this regard, the system 130 may be configured toestablish a communication link with the user input system 140, wherebythe communication link establishes a data channel (wired and/orwireless) to facilitate the transfer of data between the user inputsystem 140 and the system 130. In doing so, the system 130 may beconfigured to access one or more aspects of the user input system 140,such as, a GPS device, an image capturing component (e.g., camera), amicrophone, a speaker, and/or the like.

The user input system 140 may communicate with the system 130 (and oneor more other devices) wirelessly through communication interface 158,which may include digital signal processing circuitry. Communicationinterface 158 may provide for communications under various modes orprotocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA,TDMA, PDC, WCDMA, CDMA2000, GPRS, and/or the like. Such communicationmay occur, for example, through transceiver 160. Additionally, oralternatively, short-range communication may occur, such as using aBluetooth, Wi-Fi, and/or other such transceiver (not shown).Additionally, or alternatively, a Global Positioning System (GPS)receiver module 170 may provide additional navigation-related and/orlocation-related wireless data to user input system 140, which may beused as appropriate by applications running thereon, and in someembodiments, one or more applications operating on the system 130.

The user input system 140 may also communicate audibly using audio codec162, which may receive spoken information from a user and convert it tousable digital information. Audio codec 162 may likewise generateaudible sound for a user, such as through a speaker (e.g., in a handset)of user input system 140. Such sound may include sound from voicetelephone calls, may include recorded sound (e.g., voice messages, musicfiles, and/or the like) and may also include sound generated by one ormore applications operating on the user input system 140, and in someembodiments, one or more applications operating on the system 130.

Various implementations of the systems and techniques described here maybe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof. Suchvarious implementations may include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and/or at least one output device.

Computer programs (e.g., also referred to as programs, software,applications, code, and/or the like) may include machine instructionsfor a programmable processor, and may be implemented in a high-levelprocedural and/or object-oriented programming language, and/or inassembly/machine language. As used herein, the terms “machine-readablemedium” and/or “computer-readable medium” may refer to any computerprogram product, apparatus and/or device (e.g., magnetic discs, opticaldisks, memory, Programmable Logic Devices (PLDs), and/or the like) usedto provide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. The term “machine-readable signal” mayrefer to any signal used to provide machine instructions and/or data toa programmable processor.

To provide for interaction with a user, the systems and/or techniquesdescribed herein may be implemented on a computer having a displaydevice (e.g., a CRT (cathode ray tube), an LCD (liquid crystal display)monitor, and/or the like) for displaying information to the user, akeyboard by which the user may provide input to the computer, and/or apointing device (e.g., a mouse or a trackball) by which the user mayprovide input to the computer. Other kinds of devices may be used toprovide for interaction with a user as well. For example, feedbackprovided to the user may be any form of sensory feedback (e.g., visualfeedback, auditory feedback, and/or tactile feedback). Additionally, oralternatively, input from the user may be received in any form,including acoustic, speech, and/or tactile input.

The systems and techniques described herein may be implemented in acomputing system that includes a back end component (e.g., as a dataserver), that includes a middleware component (e.g., an applicationserver), that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usermay interact with an implementation of the systems and techniquesdescribed here), and/or any combination of such back end, middleware,and/or front end components. Components of the system may beinterconnected by any form or medium of digital data communication(e.g., a communication network). Examples of communication networksinclude a local area network (“LAN”), a wide area network (“WAN”),and/or the Internet.

In some embodiments, computing systems may include clients and servers.A client and server may generally be remote from each other andtypically interact through a communication network. The relationship ofclient and server may arise by virtue of computer programs running onthe respective computers and having a client-server relationship to eachother.

The embodiment of the system environment 100 illustrated in FIG. 1 isexemplary and other embodiments may vary. As another example, in someembodiments, the system 130 includes more, less, or differentcomponents. As another example, in some embodiments, some or all of theportions of the system environment 100, the system 130, and/or the userinput system 140 may be combined into a single portion. Likewise, insome embodiments, some or all of the portions of the system environment100, the system 130, and/or the user input system 140 may be separatedinto two or more distinct portions.

In some embodiments, the system environment may 100 include one or moreuser input systems and/or one or more resource distribution systems(e.g., similar to the system 130 and/or the user input system 140)associated with an entity (e.g., a business, a merchant, a financialinstitution, a card management institution, an software and/or hardwaredevelopment company, a software and/or hardware testing company, and/orthe like). For example, a user (e.g., an employee, a customer, and/orthe like) may use a user input system (e.g., similar to the user inputsystem 140) to provide text-based instructions to perform a resourcedistribution to a resource distribution system (e.g., similar to thesystem 130). In some embodiments, the user input system and/or theresource distribution system associated with the entity may perform oneor more of the steps described herein with respect to the process flowdescribed herein with respect to FIGS. 2 and/or 3 .

FIG. 2 illustrates a process flow 200 for automatically generatingresource distributions within a technical environment, in accordancewith an embodiment of the invention. In some embodiments, a resourcedistribution system and/or the like (e.g., similar to one or more of thesystems described herein with respect to FIG. 1 ) may perform one ormore of the steps of process flow 200.

As shown in FIG. 2 , a system performing process flow 200 may include aparser 202, which includes an input component 204, a named entityrecognition component 206, an entity identifier component 208, an entitysorter component 210, an assembler component 212, and an outputcomponent 214. In some embodiments, the parser 202 may include a machinelearning model trained using historical text-based instruction trainingdata from a historical text-based instruction training data structure216. For example, the historical text-based instruction training datamay include historical text-based instructions (e.g., email messages,SMS messages, recorded speech converted to text, text input to a chatfunction, text recognized in images, and/or the like) and historicalresource distributions generated (e.g., by a user associated with anentity and/or the like) in response to receiving the historicaltext-based instructions.

In some embodiments, the parser 202 may include a natural languageprocessing system having an artificial-intelligence-driven processingengine. Additionally, or alternatively, the system may receiveunstructured text conversations from users and process the unstructuredtext conversations through an artificial intelligence (AI) pipeline toextract user information and distribution entities (e.g., distributionelements and/or the like), such as source retainer identifiers (e.g.,account numbers and/or the like), distribution routing identifiers(e.g., routing numbers), resource amounts, distribution dates, and/orthe like. In some embodiments, the artificial-intelligence-drivenprocessing engine may use a convolutional neural network and a cascadeof regular expressions to identify distribution entities and/ordistribution elements, associate each distribution entity and/ordistribution element with a sending user or a recipient user, and groupthe distribution entities and/or distribution elements into individualresource distributions.

In some embodiments, the parser 202 may include a machine learningmodel, such as a convolutional neural network, that is initially trainedusing annotated linguistic data (e.g., the OntoNotes dataset by Spacyand/or the like) and then further trained using historical text-basedinstruction training data including historical text-based instructions(e.g., email messages, SMS messages, recorded speech converted to text,text input to a chat function, text recognized in images, and/or thelike) and historical resource distributions generated (e.g., by a userassociated with an entity and/or the like) in response to receiving thehistorical text-based instructions. For example, the convolutionalneural network may have an architecture that embeds each token into a128-dimension vector using a variation of Bloom embeddings, processesthe embeddings through a 4-layer trigram convolutional neural network togenerate a tensor representation of a document, and iterates through thetensor representation and predicts the tag on each token based on thesurrounding tokens.

In some embodiments, a regular expression component of the parser 202reads the tokens and tags of a previous component and identifies and/oroverrides tags based on regular expression rules. For example,additional source retainer identifiers may be tagged by identifying textsuch as alphanumeric sequences that are neighbored by identifier tokenssuch as “SWIFT” or “ABA” or country-specific character patterns ofinternational source retainer identifiers (e.g., international bankaccount numbers and/or the like).

Additionally, or alternatively, ambiguous distribution entities and/ordistribution elements may be further classified based on surroundingtokens. For example, the system may not be able to initially determinewhether some source retainer identifiers refer to a sending sourceretainer or a receiving source retainer. In such an example, the systemmay determine whether the source retainer identifiers refer to thesending source retainer or the receiving source retainer based onidentifying words that surround the source retainer identifiers, such as“to,” “into,” “from,” and/or the like.

In some embodiments, the system may identify complete and distinctresource distributions by grouping adjacent distribution entities and/ordistribution elements based on locations of the distribution entitiesand/or distribution elements in the text. For example, the system mayscan through the text and collect distribution entities and/ordistribution elements until a repetition occurs at which point thecurrent resource distribution is determined to be complete andsubsequent distribution entities and/or distribution elements aredetermined to be part of a next resource distribution.

In some embodiments, and as shown in FIG. 2 , the process flow 200 mayinclude receiving, using the input component 204, one or more text-basedinstructions 218. For example, the parser 202 may receive (e.g., from anupstream module) one or more text-based instructions 218 (e.g., emailmessages, SMS messages, recorded speech converted to text, text input toa chat function, text recognized in images, and/or the like) andpreprocess the one or more text-based instructions 218 into text.

As shown in FIG. 2 , the process flow 200 may include predicting, usingthe named entity recognition component 206, distribution entities and/ordistribution elements. In some embodiments, the parser 202 may predict,using the named entity recognition component 206, distribution entitiesand/or distribution elements in the text using statistical patternslearned from training using historical text-based instruction trainingdata. For example, the parser 202 may receive an email containing thefollowing text.

-   -   Please transfer $5000 to my mom's checking account and $3000 to        my checking account 141414141414.    -   Thank you.    -   Karen        In such an example, the parser 202 may, using the input        component 204, preprocess the email to obtain the text.        Additionally, or alternatively, the parser 202 may predict,        using the named entity recognition component 206, that “$5000”        and “$3000” correspond to money, amounts of resources to be        distributed, and/or the like and that “Karen” corresponds to a        user.

As shown in FIG. 2 , the process flow 200 may include identifying, usingthe entity identifier component 208, distribution entities and/ordistribution elements. In some embodiments, the parser 202 may identify,using the entity identifier component 208, distribution entities and/ordistribution elements based on regular expressions and/or based onrules. For example, using the example email text previously described,the parser 202 may identify, using the entity identifier component 208,that “to my mom's checking” and “to my checking” correspond to anaccount type to which resources are to be distributed and that“141414141414” corresponds to an account number.

As shown in FIG. 2 , the process flow 200 may include sorting, using theentity sorter component 210, distribution entities and/or distributionelements. In some embodiments, the parser 202 may sort, using the entitysorter component 210, the distribution entities and/or distributionelements into pre-defined distribution fields based on surrounding wordsand/or syntactic roots. For example, using the example email textpreviously described, the parser 202 may determine that “141414141414”corresponds to an account number to which resources are to bedistributed.

As shown in FIG. 2 , the process flow 200 may include assembling, usingthe assembler component 212, distribution fields. In some embodiments,the parser 202 may assemble, using the assembler component 212,distribution fields into a distribution based on relative locations ofthe distribution fields within the text. For example, using the exampleemail text previously described, the parser 202 may assemble, using theassembler component 212, distribution fields into two resourcedistributions such that “$5000” is combined with “to my mom's checking”in a first resource distribution and “$3000” is combined with “to mychecking” and “141414141414” in a second resource distribution.

As shown in FIG. 2 , the process flow 200 may include transmitting,using the output component 214, an output to a decision engine 220. Insome embodiments, the parser 202 may transmit, using the outputcomponent 214, an output (e.g., a structured resource distributionand/or the like) to the decision engine 220, where the output includesone or more resource distributions including distribution fields. Forexample, using the example email text previously described, the parser202 may transmit, using the output component 214, an output to thedecision engine 220, where the output includes {“to account type”:“mymom's checking”, “amount”:5000.00, “currency”:“USD”} and {“to accounttype”:“my checking”, “to account number”:“141414141414”,“amount”:3000.00, “currency”:“USD”}.

As shown in FIG. 2 , the process flow 200 may include determining, withthe decision engine 220, whether the output from the parser 202 includesactual distribution elements of a resource distribution. For example,the decision engine 220 may determine whether the output from the parser202 includes actual distribution elements (e.g., an identifier for anactual sending source retainer, an identifier for an actual receivingsource retainer, an actual amount of resources to be distributed, anactual type of resource distribution, an actual type of resources to bedistributed, an actual date on which resources are to be distributed, anactual frequency at which resources are to be distributed, an actualtype of sending source retainer, an actual type of receiving sourceretainer, distribution routing identifiers, and/or the like) of aresource distribution such that additional information beyond theinformation in the output is not required to generate a resourcedistribution. As shown in FIG. 2 , the process flow 200 may includegenerating the resource distribution 222 based on determining that theoutput from the parser 202 includes actual distribution elements.

As also shown in FIG. 2 , the process flow 200 may include, based ondetermining that the output from the parser 202 does not include actualdistribution elements, determining actual distribution elements 224. Forexample, the output from the parser 202 may include partial informationfor one or more of the distribution elements, such as only a type ofsending source retainer but not an identifier for the sending sourceretainer, only a partial identifier for the sending source retainer,and/or the like, and the process flow 200 may include determining actualdistribution elements from the partial information.

In some embodiments, and as shown in FIG. 2 , the process flow 200 mayinclude determining the actual distribution elements 224 based onhistorical user data in a historical user information data structure 226and/or historical resource distribution data in a historical resourcedistribution information data structure 228. For example, using theexample email text previously described, the process flow 200 maydetermine, based on the identification of the person “Karen” in themail, an identification of an email address (e.g., an alias) from whichthe email was sent, the output including “to account type”:“mychecking”, “to account number”:“141414141414”, and/or the like, actualdistribution elements, such as an identifier associated with a sendinguser (e.g., a client ID, a client name, and/or the like), one or moreidentifiers of one or more source retainers of the sending user and/or areceiving user, identifiers of one or more source retainers to which thesending user has recently distributed resources, and/or the like. Insome embodiments, the historical user data in the historical userinformation data structure 226 may include aliases (e.g., emailaddresses, phone numbers, and/or the like) associated with users, sourceretainers from which users may authorize resource distributions, textwhich users have used to identify one or more source retainers to whichthe users have distributed resources, and/or the like. Additionally, oralternatively, the historical resource distribution data in thehistorical resource distribution information data structure 228 mayinclude identifiers of one or more source retainers to which users havedistributed resources, amounts of resources users have distributed inhistorical resource distributions, receiving user of resourcedistributions that user have authorized, and/or the like.

In some embodiments, the historical user information data structure 226and/or the historical resource distribution information data structure228 may include data that identifies users (e.g., clients, customers,and/or the like) and aliases (e.g., email addresses and/or the like),data that maps users to source retainer officers (e.g., which may beused to determine potential source retainers), data that maps users tosource retainer roles (e.g., which may be used to define a scope ofeligible source retainers with which a user may distribute resources),and/or the like. Additionally, or alternatively, the historical userinformation data structure 226 and/or the historical resourcedistribution information data structure 228 may include data that mapsusers to other users using associations and/or by defining relationships(e.g., which may be used to define eligible users that may conductresource distributions on source retainers and/or source retainers towhich users may distribute resources), data that maps source retainersto distribution eligibility rules (e.g., which may be used to identifyeligible source retainers), and data that maps users and sourceretainers to historical resource distributions and/or may be used tocompute likelihoods of source retainers being a receiving sourceretainer for a distribution, distribution routing identifiers, and/orother distribution elements (e.g., which may be used to determinecomplete distribution elements from partial information and/or thelike).

In some embodiments, the process flow 200 may include determining, basedon historical user data in the historical user information datastructure 226 and/or historical resource distribution data in thehistorical resource distribution information data structure 228, alikelihood of one or more distribution elements being an intendeddistribution element for a resource distribution element. Additionally,or alternatively, the process flow 200 may include determining, based onhistorical user data and/or historical resource distribution data (e.g.,frequency of usage, timing of usage, and/or the like) and based onpartial information in the output from the parser 202, likelihoods ofdistribution elements being an intended distribution element. Forexample, based on a type of a source retainer in the output of theparser 202 and a user being associated with multiple source retainers ofthe type, the process flow 200 may include determining, based onfrequency of usage, timing of usage, and/or the like, a likelihood, foreach of the source retainers of the multiple source retainers, being anintended source retainer. In such an example, the process flow 200 mayinclude providing, to a user associated with an entity (e.g., forconducting the resource distribution and/or the like), identifiers ofthe multiple source retainers in an order based on the likelihoods(e.g., with the highest likelihood being listed first and/or the like).Additionally, or alternatively, the process flow 200 may includeproviding a list ordered based on likelihoods for any distributionelement (e.g., for which the output from the parser 202 included partialinformation, for which complete information cannot be definitivelydetermined, and/or the like). In some embodiments, the list may includedistribution elements ranked based on a confidence level of thedetermination and/or the like.

As shown in FIG. 2 , the process flow 200 may include, after determiningactual distribution elements 224, providing the actual distributionelements to generate a resource distribution 222. Additionally, oralternatively, the process flow 200 may include providing the actualdistribution elements and/or a list ordered based on likelihoods forother actual distribution elements to a user associated with an entity(e.g., for conducting the resource distribution and/or the like). Insome embodiments, the process flow 200 may include receiving, from theuser associated with the entity, authorization to perform the resourcedistribution and performing, based on the authorization, the resourcedistribution (e.g., distributing resources from a sending sourceretainer to a receiving source retainer and/or the like). Additionally,or alternatively, the process flow 200 may include automaticallyperforming the resource distribution (e.g., distributing resources froma sending source retainer to a receiving source retainer and/or thelike).

Process flow 200 may include additional embodiments, such as any singleembodiment or any combination of embodiments described below and/or inconnection with one or more other processes described elsewhere herein.Although FIG. 2 shows example blocks of process flow 200, in someembodiments, process flow 200 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 2 . Additionally, or alternatively, two or more of theblocks of process flow 200 may be performed in parallel.

FIG. 3 illustrate a process flow 300 for automatically generatingresource distributions within a technical environment, in accordancewith an embodiment of the invention. In some embodiments, a resourcedistribution system and/or the like (e.g., similar to one or more of thesystems described herein with respect to FIG. 1 ) may perform one ormore of the steps of process flow 300.

As shown in block 302 of FIG. 3 , the process flow 300 may includereceiving a text-based instruction (e.g., an email message, an SMSmessage, recorded speech converted to text, text input to a chatfunction, text recognized in an image, and/or the like). For example, aresource distribution system may receive one or more text-basedinstructions (e.g., from one or more users, from one or more datastructures, from one or more communication systems, and/or the like).

As shown in block 304 of FIG. 3 , the process flow 300 may includeparsing, using a machine learning model, the text-based instruction togenerate a structured resource distribution including predicteddistribution elements. For example, a resource distribution system mayparse, using a machine learning model one or more text-basedinstructions to generate, for each text-based instruction, one or morestructured resource distributions including predicted distributionelements.

As shown in block 306 of FIG. 3 , the process flow 300 may includedetermining, based on the text-based instruction, a sender alias fromwhich the text-based instruction was sent. For example, a resourcedistribution system may determine, based on one or more text-basedinstructions and for each text-based instruction, a sender alias fromwhich the text-based instruction was sent.

As shown in block 308 of FIG. 3 , the process flow 300 may includedetermining, based on user data in a user information data structure, auser associated with the sender alias. For example, a resourcedistribution system may determine, based on user data in a userinformation data structure, a user associated with the sender alias foreach text-based instruction of the one or more text-based instructions.

As shown in block 310 of FIG. 3 , the process flow 300 may includedetermining, based on user data associated with the user in the userinformation data structure and based on the predicted distributionelements, actual distribution elements. For example, a resourcedistribution system may determine, for each text-based instruction andbased on user data associated with the user in the user information datastructure and based on the predicted distribution elements, actualdistribution elements.

As shown in block 312 of FIG. 3 , the process flow 300 may includegenerating, based on the actual distribution elements, a resourcedistribution. For example, a resource distribution system may generate,based on the actual distribution elements, one or more resourcedistributions.

Process flow 300 may include additional embodiments, such as any singleembodiment or any combination of embodiments described below and/or inconnection with one or more other processes described elsewhere herein.

In a first embodiment, the text-based instruction may include an emailmessage, an SMS message, recorded speech converted to text, text inputto a chat function, text recognized in an image, and/or the like.

In a second embodiment alone or in combination with the firstembodiment, the process flow 300 may include, when parsing thetext-based instruction, parsing, using the machine learning model, thetext-based instruction to identify, based on statistical patterns, namedelements in the text-based instruction, identify, based on rules,additional elements in the text-based instruction, sort, based onsurrounding words in the text-based instruction and syntactic roots, thenamed elements and the additional elements into the predicteddistribution elements, and assemble the predicted distribution elementsinto the structured resource distribution.

In a third embodiment alone or in combination with any of the firstthrough second embodiments, the predicted distribution elements mayinclude a predicted sending source retainer, a predicted receivingsource retainer, a predicted amount of resources to be distributed, apredicted type of resource distribution, a predicted type of resourcesto be distributed, a predicted date on which resources are to bedistributed, a predicted frequency at which resources are to bedistributed, a predicted type of sending source retainer, a predictedtype of receiving source retainer, and/or the like.

In a fourth embodiment alone or in combination with any of the firstthrough third embodiments, the process flow 300 may include parsing,using the machine learning model, the text-based instruction todetermine additional information associated with the text-basedinstruction.

In a fifth embodiment alone or in combination with any of the firstthrough fourth embodiments, the additional information may include thesender alias, a recipient alias to which the text-based instruction wassent, an entity associated with the resource distribution, a name of theuser, an address of the user, content of the text-based instruction,and/or the like.

In a sixth embodiment alone or in combination with any of the firstthrough fifth embodiments, the process flow 300 may include, whendetermining the user associated with the sender alias, determining,based on the user data in the user information data structure and basedon the additional information, the user associated with the senderalias.

In a seventh embodiment alone or in combination with any of the firstthrough sixth embodiments, the process flow 300 may include, whendetermining the actual distribution elements, determining, based on theuser data in the user information data structure associated with theuser, based on the predicted distribution elements, and based on theadditional information, the actual distribution elements.

In an eighth embodiment alone or in combination with any of the firstthrough seventh embodiments, the actual distribution elements mayinclude an actual sending source retainer, an actual receiving sourceretainer, an actual amount of resources to be distributed, an actualtype of resource distribution, an actual type of resources to bedistributed, an actual date on which resources are to be distributed, anactual frequency at which resources are to be distributed, an actualtype of sending source retainer, an actual type of receiving sourceretainer, and/or the like.

In a ninth embodiment alone or in combination with any of the firstthrough eighth embodiments, the process flow 300 may include, whenparsing the text-based instruction, parsing, using the machine learningmodel, the text-based instruction to identify multiple resourcedistributions within the text-based instruction and generate, for eachresource distribution of the multiple resource distributions, astructured resource distribution.

In a tenth embodiment alone or in combination with any of the firstthrough ninth embodiments, the user information data structure includesdata that, for a plurality of users, associates users with aliases,associates users to source retainer officers, associates users to sourceretainer roles, associates users to other users, associates sourceretainers to distribution eligibility rules, associates users and sourceretainers to historical resource distributions, and/or the like.

In an eleventh embodiment alone or in combination with any of the firstthrough tenth embodiments, the process flow 300 may include, whendetermining the actual distribution elements, determining sourceretainers associated with the user, determining source retainers forwhich the user is authorized to conduct resource distributions,determining source retainers of other users associated with the user,determining source retainers eligible for resource distributions viatext-based instructions, determining source retainers associated withhistorical resource distributions conducted by the user, and/or thelike.

In a twelfth embodiment alone or in combination with any of the firstthrough eleventh embodiments, the process flow 300 may include, whendetermining the actual distribution elements, determining that apredicted distribution element, of the predicted distribution elements,is a partial distribution element and determining, based on the userdata in the user information data structure associated with the user andbased on the partial distribution element, an actual distributionelement of the actual distribution elements.

In a thirteenth embodiment alone or in combination with any of the firstthrough twelfth embodiments, the process flow 300 may include, whendetermining the actual distribution elements, determining that multiplepossible distribution elements are associated with a predicteddistribution element of the predicted distribution elements,determining, based on a frequency of usage of the multiple possibledistribution elements and based on a timing of usage of the multiplepossible distribution elements, a likelihood of each of the multiplepossible distribution elements corresponding to the predicteddistribution element, and providing, to another user and in an orderbased on the likelihood of each of the multiple possible distributionelements corresponding to the predicted distribution element, themultiple possible distribution elements for selection by the other userfor generation in the resource distribution.

In a fourteenth embodiment alone or in combination with any of the firstthrough thirteenth embodiments, the process flow 300 may includeproviding, to another user, the resource distribution for authorization.

In a fifteenth embodiment alone or in combination with any of the firstthrough fourteenth embodiments, the process flow 300 may includeperforming the resource distribution.

Although FIG. 3 shows example blocks of process flow 300, in someembodiments, process flow 300 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 3 . Additionally, or alternatively, two or more of theblocks of process flow 300 may be performed in parallel.

As noted above, in some embodiments, the process flow 200 and/or theprocess flow 300 may include performing one or more of the functionsdescribed herein using machine learning and/or a machine learning model.For example, the system may provide data from text-based instructionsand/or the like to a machine learning model trained (e.g., usinghistorical data) to output structured resource distributions.

In some embodiments, the system may be configured to implement any ofthe following applicable machine learning algorithms either singly or incombination: supervised learning (e.g., using logistic regression, usingback propagation neural networks, using random forests, decision trees,and/or the like), unsupervised learning (e.g., using an Apriorialgorithm, using K-means clustering), semi-supervised learning,reinforcement learning (e.g., using a Q-learning algorithm, usingtemporal difference learning), and any other suitable learning style.Each module of the system may implement any one or more of: a regressionalgorithm (e.g., ordinary least squares, logistic regression, stepwiseregression, multivariate adaptive regression splines, locally estimatedscatterplot smoothing, and/or the like), an instance-based method (e.g.,k-nearest neighbor, learning vector quantization, self-organizing map,and/or the like), a regularization method (e.g., ridge regression, leastabsolute shrinkage and selection operator, elastic net, and/or thelike), a decision tree learning method (e.g., classification andregression tree, iterative dichotomiser 3, C4.5, chi-squared automaticinteraction detection, decision stump, random forest, multivariateadaptive regression splines, gradient boosting machines, and/or thelike), a Bayesian method (e.g., naïve Bayes, averaged one-dependenceestimators, Bayesian belief network, and/or the like), a kernel method(e.g., a support vector machine, a radial basis function, a lineardiscriminant analysis, and/or the like), a clustering method (e.g.,k-means clustering, expectation maximization, and/or the like), anassociated rule learning algorithm (e.g., an Apriori algorithm, an Eclatalgorithm, and/or the like), an artificial neural network model (e.g., aPerceptron method, a back-propagation method, a Hopfield network method,a self-organizing map method, a learning vector quantization method,and/or the like), a deep learning algorithm (e.g., a restrictedBoltzmann machine, a deep belief network method, a convolution networkmethod, a stacked auto-encoder method, and/or the like), adimensionality reduction method (e.g., principal component analysis,partial least squares regression, Sammon mapping, multidimensionalscaling, projection pursuit, and/or the like), an ensemble method (e.g.,boosting, bootstrapped aggregation, AdaBoost, stacked generalization,gradient boosting machine method, random forest method, and/or thelike), and any suitable form of machine learning algorithm. Eachprocessing portion of the system may additionally or alternativelyleverage a probabilistic module, heuristic module, deterministic module,or any other suitable module leveraging any other suitable computationmethod, machine learning method or combination thereof. However, anysuitable machine learning approach may otherwise be incorporated in thesystem. Further, any suitable model (e.g., machine learning, non-machinelearning, and/or the like) may be used in generating data relevant tothe system. In some embodiments, the one or more machine learningalgorithms may be predictive modeling algorithms configured to use dataand statistics to predict outcomes with forecasting models.

In some embodiments, the machine learning model may be generated bytraining on data from text-based instructions, data from resourcedistributions performed based on text-based instructions, and/or thelike over a predetermined past period of time. In doing so, the systemmay be configured to output structured resource distributions includingpredicted distribution elements and/or the like. In some embodiments,the one or more machine learning algorithms may be used to calculatelikelihoods of a predicted distribution element corresponding to anactual distribution element, confidence levels that a predicteddistribution element corresponds to an actual distribution element,and/or the like.

As will be appreciated by one of ordinary skill in the art in view ofthis disclosure, the present invention may include and/or be embodied asan apparatus (including, for example, a system, machine, device,computer program product, and/or the like), as a method (including, forexample, a business method, computer-implemented process, and/or thelike), or as any combination of the foregoing. Accordingly, embodimentsof the present invention may take the form of an entirely businessmethod embodiment, an entirely software embodiment (including firmware,resident software, micro-code, stored procedures in a database, or thelike), an entirely hardware embodiment, or an embodiment combiningbusiness method, software, and hardware aspects that may generally bereferred to herein as a “system.” Furthermore, embodiments of thepresent invention may take the form of a computer program product thatincludes a computer-readable storage medium having one or morecomputer-executable program code portions stored therein. As usedherein, a processor, which may include one or more processors, may be“configured to” perform a certain function in a variety of ways,including, for example, by having one or more general-purpose circuitsperform the function by executing one or more computer-executableprogram code portions embodied in a computer-readable medium, and/or byhaving one or more application-specific circuits perform the function.

It will be understood that any suitable computer-readable medium may beutilized. The computer-readable medium may include, but is not limitedto, a non-transitory computer-readable medium, such as a tangibleelectronic, magnetic, optical, electromagnetic, infrared, and/orsemiconductor system, device, and/or other apparatus. For example, insome embodiments, the non-transitory computer-readable medium includes atangible medium such as a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a compact discread-only memory (CD-ROM), and/or some other tangible optical and/ormagnetic storage device. In other embodiments of the present invention,however, the computer-readable medium may be transitory, such as, forexample, a propagation signal including computer-executable program codeportions embodied therein.

One or more computer-executable program code portions for carrying outoperations of the present invention may include object-oriented,scripted, and/or unscripted programming languages, such as, for example,Java, Perl, Smalltalk, C#, C++, SAS, SQL, Python, Objective C,JavaScript, and/or the like. In some embodiments, the one or morecomputer-executable program code portions for carrying out operations ofembodiments of the present invention are written in conventionalprocedural programming languages, such as the “C” programming languagesand/or similar programming languages. The computer program code mayalternatively or additionally be written in one or more multi-paradigmprogramming languages, such as, for example, F#.

Some embodiments of the present invention are described herein withreference to flowchart illustrations and/or block diagrams of apparatusand/or methods. It will be understood that each block included in theflowchart illustrations and/or block diagrams, and/or combinations ofblocks included in the flowchart illustrations and/or block diagrams,may be implemented by one or more computer-executable program codeportions. These one or more computer-executable program code portionsmay be provided to a processor of a general purpose computer, specialpurpose computer, and/or some other programmable data processingapparatus in order to produce a particular machine, such that the one ormore computer-executable program code portions, which execute via theprocessor of the computer and/or other programmable data processingapparatus, create mechanisms for implementing the steps and/or functionsrepresented by the flowchart(s) and/or block diagram block(s).

The one or more computer-executable program code portions may be storedin a transitory and/or non-transitory computer-readable medium (e.g. amemory) that may direct, instruct, and/or cause a computer and/or otherprogrammable data processing apparatus to function in a particularmanner, such that the computer-executable program code portions storedin the computer-readable medium produce an article of manufactureincluding instruction mechanisms which implement the steps and/orfunctions specified in the flowchart(s) and/or block diagram block(s).

The one or more computer-executable program code portions may also beloaded onto a computer and/or other programmable data processingapparatus to cause a series of operational steps to be performed on thecomputer and/or other programmable apparatus. In some embodiments, thisproduces a computer-implemented process such that the one or morecomputer-executable program code portions which execute on the computerand/or other programmable apparatus provide operational steps toimplement the steps specified in the flowchart(s) and/or the functionsspecified in the block diagram block(s). Alternatively,computer-implemented steps may be combined with, and/or replaced with,operator- and/or human-implemented steps in order to carry out anembodiment of the present invention.

Although many embodiments of the present invention have just beendescribed above, the present invention may be embodied in many differentforms and should not be construed as limited to the embodiments setforth herein; rather, these embodiments are provided so that thisdisclosure will satisfy applicable legal requirements. Also, it will beunderstood that, where possible, any of the advantages, features,functions, devices, and/or operational aspects of any of the embodimentsof the present invention described and/or contemplated herein may beincluded in any of the other embodiments of the present inventiondescribed and/or contemplated herein, and/or vice versa. In addition,where possible, any terms expressed in the singular form herein aremeant to also include the plural form and/or vice versa, unlessexplicitly stated otherwise. Accordingly, the terms “a” and/or “an”shall mean “one or more,” even though the phrase “one or more” is alsoused herein. Like numbers refer to like elements throughout.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of and not restrictive on the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations, modifications, andcombinations of the just described embodiments may be configured withoutdeparting from the scope and spirit of the invention. Therefore, it isto be understood that, within the scope of the appended claims, theinvention may be practiced other than as specifically described herein.

What is claimed is:
 1. A system for automatically generating resourcedistributions, the system comprising: a transceiver; a communicationinterface for communicating over a network using the transceiver; aparser comprising an input component, an assembler component, and amachine learning model; a historical user information data structurecomprising data that identifies users and aliases and data that mapsusers to source retainer roles; a historical resource distributioninformation data structure comprising data that maps users to otherusers using associations and by defining relationships, data that mapssource retainers to distribution eligibility rules, data that maps usersand source retainers to historical resource distributions, anddistribution routing identifiers; at least one non-transitory storagedevice; and at least one processing device coupled to the transceiver,the communication interface, the parser, the historical user informationdata structure, the historical resource distribution information datastructure, and the at least one non-transitory storage device, whereinthe at least one non-transitory storage device comprisescomputer-executable program code comprising instructions configured tocause the at least one processing device to perform operationscomprising: train, using annotated linguistic data and historicaltext-based instruction training data, the machine learning model of theparser to identify distribution entities and distribution elements andassociate each distribution entity and distribution element with asending user or a recipient user, wherein the historical text-basedinstruction training data comprises historical text-based instructionsand historical resource distributions generated in response to receivingthe historical text-based instructions; receive, using the communicationinterface, the transceiver, and the input component of the parser, atext-based instruction from a user device via the network; determine,using the machine learning model and based on the text-basedinstruction, a sender alias from which the text-based instruction wassent; determine, based on the data in the historical user informationdata structure, a user associated with the sender alias; determine,using the machine learning model and based on the text-basedinstruction, predicted distribution elements; assemble, using theassembler component of the parser, the predicted distribution elementsinto a resource distribution; determine, based on the data in thehistorical resource distribution information data structure associatedwith the user and based on the predicted distribution elements, actualdistribution elements in the resource distribution; and generate, basedon the actual distribution elements, the resource distribution.
 2. Thesystem of claim 1, wherein the text-based instruction comprises at leastone of an email message, an SMS message, recorded speech converted totext, text input to a chat function, or text recognized in an image. 3.The system of claim 1, wherein the predicted distribution elementscomprise at least one of a predicted sending source retainer, apredicted receiving source retainer, a predicted amount of resources tobe distributed, a predicted type of resource distribution, a predictedtype of resources to be distributed, a predicted date on which resourcesare to be distributed, a predicted frequency at which resources are tobe distributed, a predicted type of sending source retainer, or apredicted type of receiving source retainer.
 4. The system of claim 1,wherein the computer-executable program code comprises instructionsconfigured to cause the at least one processing device to parse, usingthe machine learning model, the text-based instruction to determineadditional information associated with the text-based instruction. 5.The system of claim 4, wherein the additional information comprises atleast one of the sender alias, a recipient alias to which the text-basedinstruction was sent, an entity associated with the resourcedistribution, a name of the user, an address of the user, or content ofthe text-based instruction.
 6. The system of claim 5, wherein thecomputer-executable program code comprises instructions configured tocause the at least one processing device to, when determining the userassociated with the sender alias, determine, based on the data in thehistorical user information data structure and based on the additionalinformation, the user associated with the sender alias.
 7. The system ofclaim 5, wherein the computer-executable program code comprisesinstructions configured to cause the at least one processing device to,when determining the actual distribution elements, determine, based onthe data in the historical resource distribution information datastructure associated with the user, based on the predicted distributionelements, and based on the additional information, the actualdistribution elements.
 8. The system of claim 1, wherein the actualdistribution elements comprise at least one of an actual sending sourceretainer, an actual receiving source retainer, an actual amount ofresources to be distributed, an actual type of resource distribution, anactual type of resources to be distributed, an actual date on whichresources are to be distributed, an actual frequency at which resourcesare to be distributed, an actual type of sending source retainer, or anactual type of receiving source retainer.
 9. The system of claim 1,wherein the historical user information data structure comprises datathat, for a plurality of users: associates users with aliases;associates users to source retainer officers; associates users to sourceretainer roles; associates users to other users; associates sourceretainers to distribution eligibility rules; or associates users andsource retainers to historical resource distributions.
 10. The system ofclaim 9, wherein the computer-executable program code comprisesinstructions configured to cause the at least one processing device to,when determining the actual distribution elements; determine sourceretainers associated with the user; determine source retainers for whichthe user is authorized to conduct resource distributions; determinesource retainers of other users associated with the user; determinesource retainers eligible for resource distributions via text-basedinstructions; and determine source retainers associated with historicalresource distributions conducted by the user.
 11. The system of claim 1,wherein the computer-executable program code comprises instructionsconfigured to cause the at least one processing device to, whendetermining the actual distribution elements: determine that a predicteddistribution element, of the predicted distribution elements, is apartial distribution element; and determine, based on the data in thehistorical resource distribution information data structure associatedwith the user and based on the partial distribution element, an actualdistribution element of the actual distribution elements.
 12. The systemof claim 1, comprising a display device, wherein the computer-executableprogram code comprises instructions configured to cause the at least oneprocessing device to, when determining the actual distribution elements:determine that multiple possible distribution elements are associatedwith a predicted distribution element of the predicted distributionelements; determine, based on a frequency of usage of the multiplepossible distribution elements and based on a timing of usage of themultiple possible distribution elements, a likelihood of each of themultiple possible distribution elements corresponding to the predicteddistribution element; and display, using the display device and toanother user, a graphical user interface comprising the multiplepossible distribution elements in an order based on the likelihood ofeach of the multiple possible distribution elements corresponding to thepredicted distribution element for selection by the other user forgeneration in the resource distribution.
 13. The system of claim 1,wherein the computer-executable program code comprises instructionsconfigured to cause the at least one processing device to provide, toanother user, the resource distribution for authorization.
 14. Thesystem of claim 1, wherein the computer-executable program codecomprises instructions configured to cause the at least one processingdevice to perform the resource distribution.
 15. A computer programproduct for automatically generating resource distributions comprising anon-transitory computer-readable medium comprising code causing a firstapparatus to: train, using annotated linguistic data and historicaltext-based instruction training data, a machine learning model of aparser of the first apparatus to identify distribution entities anddistribution elements and associate each distribution entity anddistribution element with a sending user or a recipient user, whereinthe historical text-based instruction training data comprises historicaltext-based instructions and historical resource distributions generatedin response to receiving the historical text-based instructions;receive, using a communication interface of the first apparatus, atransceiver of the first apparatus, and an input component of the parserof the first apparatus, a text-based instruction from a user device viaa network, wherein the parser comprises the input component, anassembler component, and the machine learning model; determine, usingthe machine learning model and based on the text-based instruction, asender alias from which the text-based instruction was sent; determine,based on data in a historical user information data structure of thefirst apparatus, a user associated with the sender alias, wherein thehistorical user information data structure comprises data thatidentifies users and aliases and data that maps users to source retainerroles; determine, using the machine learning model and based on thetext-based instruction, predicted distribution elements; assemble, usingthe assembler component of the parser, the predicted distributionelements into a resource distribution; determine, based on data in ahistorical resource distribution information data structure of the firstapparatus associated with the user and based on the predicteddistribution elements, actual distribution elements in the resourcedistribution, wherein the historical resource distribution informationdata structure comprises data that maps users to other users usingassociations and by defining relationships, data that maps sourceretainers to distribution eligibility rules, data that maps users andsource retainers to historical resource distributions, and distributionrouting identifiers; and generate, based on the actual distributionelements, the resource distribution.
 16. The computer program product ofclaim 15, wherein the text-based instruction comprises at least one ofan email message, an SMS message, recorded speech converted to text,text input to a chat function, or text recognized in an image.
 17. Thecomputer program product of claim 15, wherein the predicted distributionelements comprise at least one of a predicted sending source retainer, apredicted receiving source retainer, a predicted amount of resources tobe distributed, a predicted type of resource distribution, a predictedtype of resources to be distributed, a predicted date on which resourcesare to be distributed, a predicted frequency at which resources are tobe distributed, a predicted type of sending source retainer, or apredicted type of receiving source retainer.
 18. The computer programproduct of claim 15, wherein the non-transitory computer-readable mediumcomprises code causing the first apparatus to parse, using the machinelearning model, the text-based instruction to determine additionalinformation associated with the text-based instruction.
 19. The computerprogram product of claim 18, wherein the additional informationcomprises at least one of the sender alias, a recipient alias to whichthe text-based instruction was sent, an entity associated with theresource distribution, a name of the user, an address of the user, orcontent of the text-based instruction.
 20. A method for automaticallygenerating resource distributions, the method comprising: training,using annotated linguistic data and historical text-based instructiontraining data, a machine learning model of a parser of a first apparatusto identify distribution entities and distribution elements andassociate each distribution entity and distribution element with asending user or a recipient user, wherein the historical text-basedinstruction training data comprises historical text-based instructionsand historical resource distributions generated in response to receivingthe historical text-based instructions; receiving, using a communicationinterface of the first apparatus, a transceiver of the first apparatus,and an input component of the parser of the first apparatus, atext-based instruction from a user device via a network, wherein theparser comprises the input component, an assembler component, and themachine learning model; determining, using the machine learning modeland based on the text-based instruction, a sender alias from which thetext-based instruction was sent; determining, based on data in ahistorical user information data structure of the first apparatus, auser associated with the sender alias, wherein the historical userinformation data structure comprises data that identifies users andaliases and data that maps users to source retainer roles; determining,using the machine learning model and based on the text-basedinstruction, predicted distribution elements; assembling, using theassembler component of the parser, the predicted distribution elementsinto a resource distribution; determining, based on data in a historicalresource distribution information data structure of the first apparatusassociated with the user and based on the predicted distributionelements, actual distribution elements in the resource distribution,wherein the historical resource distribution information data structurecomprises data that maps users to other users using associations and bydefining relationships, data that maps source retainers to distributioneligibility rules, data that maps users and source retainers tohistorical resource distributions, and distribution routing identifiers;and generating, based on the actual distribution elements, the resourcedistribution.